Why does a 95% CI not imply a 95% chance of containing the mean?

Question

It seems that through various related questions here, there is consensus that the "95%" part of what we call a "95% confidence interval" refers to the fact that if we were to exactly replicate our sampling and CI-computation procedures many times, 95% of thusly computed CIs would contain the population mean. It also seems to be the consensus that this definition does not permit one to conclude from a single 95%CI that there is a 95% chance that the mean falls somewhere within the CI. However, I don't understand how the former doesn't imply the latter insofar as, having imagined many CIs 95% of which contain the population mean, shouldn't our uncertainty (with regards to whether our actually-computed CI contains the population mean or not) force us to use the base-rate of the imagined cases (95%) as our estimate of the probability that our actual case contains the CI?

I've seen posts argue along the lines of "the actually-computed CI either contains the population mean or it doesn't, so its probability is either 1 or 0", but this seems to imply a strange definition of probability that is dependent on unknown states (i.e. a friend flips fair coin, hides the result, and I am disallowed from saying there is a 50% chance that it's heads).

Surely I'm wrong, but I don't see where my logic has gone awry...

Answer 1

Part of the issue is that the frequentist definition of a probability doesn't allow a nontrivial probability to be applied to the outcome of a particular experiment, but only to some fictitious population of experiments from which this particular experiment can be considered a sample. The definition of a CI is confusing as it is a statement about this (usually) fictitious population of experiments, rather than about the particular data collected in the instance at hand. So part of the issue is one of the definition of a probability: The idea of the true value lying within a particular interval with probability 95% is inconsistent with a frequentist framework.

Another aspect of the issue is that the calculation of the frequentist confidence doesn't use all of the information contained in the particular sample relevant to bounding the true value of the statistic. My question "Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals" discusses a paper by Edwin Jaynes which has some really good examples that really highlight the difference between confidence intervals and credible intervals. One that is particularly relevant to this discussion is Example 5, which discusses the difference between a credible and a confidence interval for estimating the parameter of a truncated exponential distribution (for a problem in industrial quality control). In the example he gives, there is enough information in the sample to be certain that the true value of the parameter lies nowhere in a properly constructed 90% confidence interval!

This may seem shocking to some, but the reason for this result is that confidence intervals and credible intervals are answers to two different questions, from two different interpretations of probability.

The confidence interval is the answer to the request: "Give me an interval that will bracket the true value of the parameter in $100p$% of the instances of and experiment that is repeated a large number of times." The credible interval is an answer to the request: "Give me an interval that brackets the true value with probability $p$ given the particular sample I've actually observed." To be able to answer the latter request, we must first adopt either (a) a new concept of the data generating process or (b) a different concept of the definition of probability itself.

The main reason that any particular 95% confidence interval does not imply a 95% chance of containing the mean is because the confidence interval is an answer to a different question, so it is only the right answer when the answer to the two questions happens to have the same numerical solution.

In short, credible and confidence intervals answer different questions from different perspectives; both are useful, but you need to choose the right interval for the question you actually want to ask. If you want an interval that admits an interpretation of a 95% (posterior) probability of containing the true value, then choose a credible interval (and, with it, the attendant conceptualization of probability), not a confidence interval. The thing you ought not to do is to adopt a different definition of probability in the interpretation than that used in the analysis.

Thanks to cardinal for his refinements!

Answer 2

Probabilities related to theoretical long run events, that even work if you run the events in the long run, just don't relate to a single event after it's done.

You wanted to compare it to the probability of a hidden coin being heads.. maybe I can work with that. Let's see if I can completely screw this up sticking to your analogy.

If you don't calculate your confidence interval you've got something similar to the hidden coin and it has a 95% probability of containing mu just like the coin has a 50% probability of being heads. Once you produce the coin there is no probability that it's heads, it's either heads or it's not. What are the odds that you'd place on a wager about a coin that I already showed you came up heads? Does that have any relation to the probability of it coming up heads on the next flip, or that I could have predicted that head? No. The process by which the head is produced has a 0.5 probability of producing them but it does not mean that a head that already exists has a 0.5 probability of being. Once you calculate your CI there is no probability that it captures mu, it either does or it doesn't—you've revealed the coin.

OK, I think I've tortured that enough.

The thing that makes the CI confusing is that it isn't heads or tails, it is a range of values. They either do or don't contain mu. We think they likely contain mu but the probability of that isn't the same as the process that went into developing it. The 95% part of the 95% CI name is just about the process.

It's better to think of the name 95% CI as a designation of a kind of measurement of a range of values that you think plausibly contain mu. We could call it the Jennifer CI while the 99% CI is the Wendy CI. That might actually be better. Then, afterwards we can say that we believe mu is likely to be in the range of values and no one would get stuck saying that there is a Wendy probability that we've captured mu. Ok, they might say that, but then the error would be more obvious.

Answer 3

Formal, explicit ideas about arguments, inference and logic originated, within the Western tradition, with Aristotle. Aristotle wrote about these topics in several different works (including one called the Topics ;-) ). However, the most basic single principle is The Law of Non-contradiction, which can be found in various places, including Metaphysics book IV, chapters 3 & 4. A typical formulation is: " ...it is impossible for anything at the same time to be and not to be [in the same sense]" (1006 a 1). Its importance is stated slightly earlier, " ...this is naturally the starting-point even for all the other axioms" (1005 b 30). Forgive me for waxing philosophical, but this question by its nature has philosophical content that cannot simply be pushed aside for convenience.

Consider this thought-experiment: Alex flips a coin, catches it and turns it over onto his forearm with his hand covering the side facing up. Bob was standing in just the right position; he briefly saw the coin in Alex's hand, and thus can deduce which side is facing up now. However, Carlos did not see the coin--he wasn't in the right spot. At this point, Alex asks them what the probability is that the coin shows heads. Carlos suggests that the probability is .5, as that is the long-run frequency of heads. Bob disagrees, he confidently asserts that the probability is nothing else but exactly 0.

Now, who is right? It is possible, of course, that Bob mis-saw and is incorrect (let us assume that he did not mis-see). Nonetheless, you cannot hold that both are right and hold to the law of non-contradiction. (I suppose that if you don't believe in the law of non-contradiction, you could think they're both right, or some other such formulation.) Now imagine a similar case, but without Bob present, could Carlos' suggestion be more right (eh?) without Bob around, since no one saw the coin? The application of the law of non-contradiction is not quite as clear in this case, but I think it is obvious that the parts of the situation that seem to be important are held constant from the former to the latter. There have been many attempts to define probability, and in the future there may still yet be many more, but a definition of probability as a function of who happens to be standing around and where they happen to be positioned has little appeal. At any rate (guessing by your use of the phrase "confidence interval"), we are working within the Frequentist approach, and therein whether anyone knows the true state of the coin is irrelevant. It is not a random variable--it is a realized value and either it shows heads, or it shows tails.

As @John notes, the state of a coin may not at first seem similar to the question of whether a confidence interval covers the true mean. However, instead of a coin, we can understand this abstractly as a realized value drawn from a Bernouli distribution with parameter $p$. In the coin situation, $p=.5$, whereas for a 95% CI, $p=.95$. What's important to realize in making the connection is that the important part of the metaphor isn't the $p$ that governs the situation, but rather that the flipped coin or the calculated CI is a realized value, not a random variable.

It is important for me to note at this point that all of this is the case within a Frequentist conception of probability. The Bayesian perspective does not violate the law of non-contradiction, it simply starts from different metaphysical assumptions about the nature of reality (more specifically about probability). Others on CV are much better versed in the Bayesian perspective than I am, and perhaps they may explain why the assumptions behind your question do not apply within the Bayesian approach, and that in fact, there may well be a 95% probability of the mean lying within a 95% credible interval, under certain conditions including (among others) that the prior used was accurate (see the comment by @DikranMarsupial below). However, I think all would agree, that once you state you are working within the Frequentist approach, it cannot be the case that the probability of the true mean lying within any particular 95% CI is .95.

Answer 4

I'm surprised that no one has brought up Berger's example of an essentially useless 75% confidence interval described in the second chapter of "The Likelihood Principle". The details can be found in the original text (which is available for free on Project Euclid): what is essential about the example is that it describes, unambiguously, a situation in which you know with absolute certainty the value of an ostensibly unknown parameter after observing data, but you would assert that you have only 75% confidence that your interval contains the true value. Working through the details of that example was what enabled me to understand the entire logic of constructing confidence intervals.

Answer 5

Say that the CI you calculated from the particular set of data you have is one of the 5% of possible CIs that does not contain the mean. How close is it to being the 95% credible interval that you would like to imagine it to be? (That is, how close is it to containing the mean with 95% probability?) You have no assurance that it's close at all. In fact, your CI may not overlap with even a single one of the 95% of 95% CIs which do actually contain the mean. Not to mention that it doesn't contain the mean itself, which also suggests it's not a 95% credible interval.

Maybe you want to ignore this and optimistically assume that your CI is one of the 95% that does contain the mean. OK, what do we know about your CI, given that it's in the 95%? That it contains the mean, but perhaps only way out at the extreme, excluding everything else on the other side of the mean. Not likely to contain 95% of the distribution.

Either way, there's no guarantee, perhaps not even a reasonable hope that your 95% CI is a 95% credible interval.

Answer 6

There are some interesting answers here, but I thought I'd add a little hands-on demonstration using R. We recently used this code in a stats course to highlight how confidence intervals work. Here's what the code does:

1 - It samples from a known distribution (n=1000)

2 - It calculates the 95% CI for the mean of each sample

3 - It asks whether or not each sample's CI includes the true mean.

4 - It reports in the console the fraction of CIs that included the true mean.

I just ran the script a bunch of times and it's actually not too uncommon to find that less than 94% of the CIs contained the true mean. At least to me, this helps dispel the idea that a confidence interval has a 95% probability of containing the true parameter.

#   In the following code, we simulate the process of
#   sampling from a distribution and calculating
#   a confidence interval for the mean of that 
#   distribution.  How often do the confidence
#   intervals actually include the mean? Let's see!
#
#   You can change the number of replicates in the
#   first line to change the number of times the 
#   loop is run (and the number of confidence intervals
#   that you simulate).
#
#   The results from each simulation are saved to a
#   data frame.  In the data frame, each row represents
#   the results from one simulation or replicate of the 
#   loop.  There are three columns in the data frame, 
#   one which lists the lower confidence limits, one with
#   the higher confidence limits, and a third column, which
#   I called "Valid" which is either TRUE or FALSE
#   depending on whether or not that simulated confidence
#   interval includes the true mean of the distribution.
#
#   To see the results of the simulation, run the whole
#   code at once, from "start" to "finish" and look in the
#   console to find the answer to the question.    

#   "start"

replicates <- 1000

conf.int.low <- rep(NA, replicates)
conf.int.high <- rep(NA, replicates)
conf.int.check <- rep(NA, replicates)

for (i in 1:replicates) {

        n <- 10
        mu <- 70
        variance <- 25
        sigma <- sqrt(variance)
        sample <- rnorm(n, mu, sigma)
        se.mean <- sigma/sqrt(n)
        sample.avg <- mean(sample)
        prob <- 0.95
        alpha <- 1-prob
        q.alpha <- qnorm(1-alpha/2)
        low.95 <- sample.avg - q.alpha*se.mean
        high.95 <- sample.avg + q.alpha*se.mean

        conf.int.low[i] <- low.95
        conf.int.high[i] <- high.95
        conf.int.check[i] <- low.95 < mu & mu < high.95
 }    

# Collect the intervals in a data frame
ci.dataframe <- data.frame(
        LowerCI=conf.int.low,
        UpperCI=conf.int.high, 
        Valid=conf.int.check
        )

# Take a peak at the top of the data frame
head(ci.dataframe)

# What fraction of the intervals included the true mean?
ci.fraction <- length(which(conf.int.check, useNames=TRUE))/replicates
ci.fraction

    #   "finish"

Hope this helps!